def prune_benchmark():
logger = get_logger("prune_pong_agent_benchmark")
prune_model = DQNPacman(input_size=prune_config.input_size,
output_size=prune_config.output_size,
model_path=prune_config.model_path,
scope=prune_config.scope,
epsilon_stop=prune_config.final_epsilon,
epsilon=prune_config.initial_epsilon,
pruning_end=prune_config.pruning_end,
pruning_freq=prune_config.pruning_freq,
sparsity_end=prune_config.sparsity_end,
target_sparsity=prune_config.target_sparsity,
prune_till_death=True)
target_model = PacmanTargetNet(input_size=dense_config.input_size,
output_size=dense_config.output_size)
logger.info("loading models")
print("loading models")
target_model.load_model(dense_config.ready_path)
prune_model.load_model(dense_config.ready_path)
prune_model.change_loss_to_benchmark_loss()
prune_model.reset_global_step()
logger.info("Commencing iterative pruning")
sparsity_vs_accuracy = iterative_pruning(logger,
prune_model,
target_model,
prune_config.n_epoch,
benchmarking=True)
print("benchmark finished")
plot_graph(sparsity_vs_accuracy,
"sparsity_vs_accuracy_benchmark",
figure_num=1)
def main():
prune_model = SimpleNet(input_size=prune_config.input_size,
output_size=prune_config.output_size,
model_path=FLAGS.model_path,
pruning_start=0,
pruning_end=100000,
target_sparsity=FLAGS.goal_sparsity,
dropout=FLAGS.dropout,
pruning_freq=FLAGS.prune_freq,
initial_sparsity=prune_config.initial_sparsity,
sparsity_start=prune_config.sparsity_start,
sparsity_end=FLAGS.end_sparsity,
scope='SimpleNetPruned')
prune_model.load_model(FLAGS.ready_path)
prune_model.reset_global_step()
# important to allow a stable pruning, the pruning mechanism takes the global step as a parameter
sparsity_vs_accuracy = prune_model.fit(
n_epochs=FLAGS.n_epochs,
learning_rate_schedule=prune_config.learning_rate_schedule,
batch_size=FLAGS.batch_size,
prune=True,
config=prune_config)
# the fit function allow gentle pruning along with fine-tuning to reach maximum sparsity and accuracy possible
plot_graph(sparsity_vs_accuracy, "sparsity_vs_accuracy_simplenet")
prune_model.load_model(FLAGS.best_path)
plot_conv_weights(prune_model, title='after')
prune_model.sess.close()
def testEnv():
env = Env()
channelThroughPut = 0 # fraction of time that packets are successfully delivered over the channel
# i.e no collisions or idle time slots
for iteration in range(config.Iterations):
for t in range(config.TimeSlots):
initialState = env.reset()
for user in range(config.N):
action = slottedAlohaProtocol()
env.step(action=action, user=user)
# each user changes the inner state of the environment where the environment uses the inner state
# in order to keep track on the channels and the ACK signals for each user
nextStateForEachUser, rewardForEachUser = env.getNextState()
# if a reward is one that means that a packets was successfully delivered over the channel
# the sum has a maximum of the number of channels -> config.K
channelThroughPut = channelThroughPut + np.sum(rewardForEachUser)
# measuring the expected value
channelThroughPut = channelThroughPut / (config.Iterations *
config.TimeSlots)
print("Channel Utilization average {}".format(channelThroughPut))
ToPlotX = range(config.Iterations * config.TimeSlots)
ToPlotY = np.ones_like(ToPlotX) * channelThroughPut
plot_graph(data=[ToPlotX, ToPlotY],
filename="Aloha",
title="Aloha",
xlabel="Time slot",
ylabel="Average channel utilization",
legend="SlottedAloha")
#
#
# def testTimeEnv():
# env = TimeDependentEnv()
# channelThroughPut = 0 # fraction of time that packets are successfully delivered over the channel
# # i.e no collisions or idle time slots
# for iteration in range(config.Iterations):
# TimeSPU = env.reset()
# for t in range(config.TimeSlots):
# env.resetTimeStep()
# # reset the internal state of the environment
# # which keep tracks of the users actions through out the time step
# for user in range(config.N):
# action = slottedAlohaProtocol()
# env.step(action=action, user=user)
# # each user changes the inner state of the environment where the environment uses the inner state
# # in order to keep track on the channels and the ACK signals for each user
# nextStateForEachUser, rewardForEachUser = env.tstep(timestep=t)
# # if a reward is one that means that a packets was successfully delivered over the channel
# # the sum has a maximum of the number of channels -> config.K
# channelThroughPut = channelThroughPut + np.sum(rewardForEachUser)
# # measuring the expected value
# channelThroughPut = channelThroughPut / (config.Iterations * config.TimeSlots)
# print("Channel Utilization average {}".format(channelThroughPut))
# ToPlotX = range(config.Iterations * config.TimeSlots)
# ToPlotY = np.ones_like(ToPlotX) * channelThroughPut
# plot_graph(data=[ToPlotX, ToPlotY], filename="Aloha", title="Aloha",
# xlabel="Time slot", ylabel="Average channel utilization", legend="SlottedAloha")
def main():
# ----------------- Setting initial variables Section -----------------
logger = get_logger(FLAGS.PoPS_dir + "/PoPS_ITERATIVE")
logger.info(" ------------- START: -------------")
logger.info("Setting initial data structures")
accuracy_vs_size = [[], []]
logger.info("Loading models")
teacher = CartPoleDQNTarget(input_size=dense_config.input_size,
output_size=dense_config.output_size)
teacher.load_model(path=FLAGS.teacher_path) # load teacher
logger.info("----- evaluating teacher -----")
print("----- evaluating teacher -----")
teacher_score = evaluate(agent=teacher, n_epoch=FLAGS.eval_epochs)
logger.info("----- teacher evaluated with {} ------".format(teacher_score))
print("----- teacher evaluated with {} -----".format(teacher_score))
prune_step_path = FLAGS.PoPS_dir + "/prune_step_"
policy_step_path = FLAGS.PoPS_dir + "/policy_step_"
initial_path = policy_step_path + "0"
logger.info(
"creating policy step 0 model, which is identical in size to the original model"
)
copy_weights(
output_path=initial_path,
teacher_path=FLAGS.teacher_path) # inorder to create the initial model
compressed_agent = StudentCartpole(
input_size=student_config.input_size,
output_size=student_config.output_size,
model_path=initial_path,
tau=student_config.tau,
pruning_freq=student_config.pruning_freq,
sparsity_end=student_config.sparsity_end,
target_sparsity=student_config.target_sparsity)
compressed_agent.load_model()
initial_size = compressed_agent.get_number_of_nnz_params()
accuracy_vs_size[0].append(initial_size)
accuracy_vs_size[1].append(teacher_score)
initial_number_of_params_at_each_layer = compressed_agent.get_number_of_nnz_params_per_layer(
)
initial_number_of_nnz = sum(initial_number_of_params_at_each_layer)
converge = False
iteration = 0
convergence_information = deque(maxlen=2)
convergence_information.append(100)
precent = 100
arch_type = 0
last_measure = initial_size
while not converge:
iteration += 1
print("----- Pruning Step {} -----".format(iteration))
logger.info(" ----- Pruning Step {} -----".format(iteration))
path_to_save_pruned_model = prune_step_path + str(iteration)
# ----------------- Pruning Section -----------------
if arch_type == 2:
arch_type = 3 # special arch_type for prune-oriented learning rate
sparsity_vs_accuracy = iterative_pruning_policy_distilliation(
logger=logger,
agent=compressed_agent,
target_agent=teacher,
iterations=FLAGS.iterations,
config=student_config,
best_path=path_to_save_pruned_model,
arch_type=arch_type,
lower_bound=student_config.LOWER_BOUND,
accumulate_experience_fn=accumulate_experience_cartpole,
evaluate_fn=evaluate,
objective_score=student_config.OBJECTIVE_SCORE)
plot_graph(data=sparsity_vs_accuracy,
name=FLAGS.PoPS_dir +
"/initial size {}%, Pruning_step number {}".format(
precent, iteration),
figure_num=iteration)
# loading model which has reasonable score with the highest sparsity
compressed_agent.load_model(path_to_save_pruned_model)
# ----------------- Measuring redundancy Section -----------------
# the amount of parameters that are not zero at each layer
nnz_params_at_each_layer = compressed_agent.get_number_of_nnz_params_per_layer(
)
# the amount of parameters that are not zero
nnz_params = sum(nnz_params_at_each_layer)
# redundancy is the parameters we dont need, nnz_params / initial is the params we need the opposite
redundancy = (1 - nnz_params / initial_number_of_nnz) * 100
print(
"----- Pruning Step {} finished, got {}% redundancy in net params -----"
.format(iteration, redundancy))
logger.info(
"----- Pruning Step {} finished , got {}% redundancy in net params -----"
.format(iteration, redundancy))
logger.info(
"----- Pruning Step {} finished with {} NNZ params at each layer".
format(iteration, nnz_params_at_each_layer))
print(
" ----- Evaluating redundancy at each layer Step {}-----".format(
iteration))
logger.info(
" ----- Evaluating redundancy at each layer Step {} -----".format(
iteration))
redundancy_at_each_layer = calculate_redundancy(
initial_nnz_params=initial_number_of_params_at_each_layer,
next_nnz_params=nnz_params_at_each_layer)
logger.info(
"----- redundancy for each layer at step {} is {} -----".format(
iteration, redundancy_at_each_layer))
if iteration == 1:
redundancy_at_each_layer = [
0.83984375, 0.8346405029296875, 0.83795166015625, 0.83984375
]
# ----------------- Policy distillation Section -----------------
print(
" ----- Creating Model with size according to the redundancy at each layer ----- "
)
logger.info(
"----- Creating Model with size according to the redundancy at each layer -----"
)
policy_distilled_path = policy_step_path + str(iteration)
# creating the compact model where every layer size is determined by the redundancy measure
compressed_agent = StudentCartpole(
input_size=student_config.input_size,
output_size=student_config.output_size,
model_path=policy_distilled_path,
tau=student_config.tau,
redundancy=redundancy_at_each_layer,
pruning_freq=student_config.pruning_freq,
sparsity_end=student_config.sparsity_end,
target_sparsity=student_config.target_sparsity,
last_measure=last_measure)
nnz_params_at_each_layer = compressed_agent.get_number_of_nnz_params_per_layer(
)
logger.info(
"----- Step {} ,Created Model with {} NNZ params at each layer".
format(iteration, nnz_params_at_each_layer))
iterative_size = compressed_agent.get_number_of_nnz_params()
last_measure = iterative_size
precent = (iterative_size / initial_size) * 100
convergence_information.append(precent)
print(
" ----- Step {}, Created Model with size {} which is {}% from original size ----- "
.format(iteration, iterative_size, precent))
logger.info(
"----- Created Model with size {} which is {}% from original size -----"
.format(iterative_size, precent))
# scheduling the right learning rate for the size of the model
if precent > 40:
arch_type = 0
elif 10 <= precent <= 40:
arch_type = 1
else:
arch_type = 2
print(" ----- policy distilling Step {} ----- ".format(iteration))
logger.info("----- policy distilling Step {} -----".format(iteration))
fit_supervised(logger=logger,
arch_type=arch_type,
student=compressed_agent,
teacher=teacher,
n_epochs=FLAGS.n_epoch,
evaluate_fn=evaluate,
accumulate_experience_fn=accumulate_experience_cartpole,
lower_score_bound=student_config.LOWER_BOUND,
objective_score=student_config.OBJECTIVE_SCORE)
policy_distilled_score = evaluate(agent=compressed_agent,
n_epoch=FLAGS.eval_epochs)
compressed_agent.reset_global_step()
print(
" ----- policy distilling Step {} finished with score {} ----- ".
format(iteration, policy_distilled_score))
logger.info(
"----- policy distilling Step {} finished with score {} -----".
format(iteration, policy_distilled_score))
# checking convergence
converge = check_convergence(convergence_information)
# for debugging purposes
accuracy_vs_size[0].append(iterative_size)
accuracy_vs_size[1].append(policy_distilled_score)
plot_graph(data=accuracy_vs_size,
name=FLAGS.PoPS_dir + "/accuracy_vs_size",
figure_num=iteration + 1,
xaxis='NNZ params',
yaxis='Accuracy')
def main():
#variables used for models logics
raw_scores, states, actions_booleans = [BEGINING_SCORE], [], []
episode_number = 0
update_weights = False # if too much time passed, update the weights even if the game is not finished
grads_sums = get_empty_grads_sums(
) # initialize the gradients holder for the trainable variables
#variables for debugging:
manual_prob_use = 0 #consider use the diffrences from 1
prob_deviation_sum = 0
default_data_counter = 0 # counts number of exceptions in reading the observations' file (and getting a default data)
step_counter = 0 #for tests
#variables for evaluation:
best_average_score = 0
current_average_score = 0
average_scores_along_the_game = []
with tf.Session() as sess:
sess.run(init)
#sess.run(init2) #check if this necessary
# check if file is not empty
if (os.path.isfile(WEIGHTS_FILE) and LOAD_WEIGHTS):
# Load with shmickle
with open(WEIGHTS_FILE, 'rb') as f: # BEST_WEIGHTS
for var, val in zip(tvars, pkl.load(f)):
sess.run(tf.assign(var, val))
print("loaded weights successfully!")
# creates file if it doesn't exisits:
if (not os.path.isfile(WEIGHTS_FILE)):
open(WEIGHTS_FILE, 'a').close()
if (not os.path.isfile(BEST_WEIGHTS)):
open(BEST_WEIGHTS, 'a').close()
print("created weights file sucessfully!")
while episode_number < MAX_GAMES:
start_time = time.time()
#get data and process score to reward
obsrv, score, bonus, is_dead, request_id, default_obsrv, AI_action, AI_accel = get_observation(
) # get observation
#if from some reason the bot died and the message for it got lost, we check it here.
if (not is_dead):
is_dead = check_if_died(raw_scores[-1], score)
#if default takes the score from last step
if (score == 0):
score = raw_scores[-1]
raw_scores.append(score)
#FOR DEBUGGING
#is_dead = False
default_data_counter += default_obsrv
#vars = sess.run(tvars)
# append the relevant observation to the following action, to states
states.append(obsrv)
# Run the policy network and get a distribution over actions
action_probs = sess.run(actions_probs,
feed_dict={observations: [obsrv]})
# if - exploration, else - explotation
if (np.random.binomial(1, EPSILON_FOR_EXPLORATION, 1)[0]):
chosen_actions = pick_action_uniformly(action_probs[0])
logger.write_to_log('Tried exploration!')
else:
# np.random.multinomial cause problems
try:
chosen_actions = np.random.multinomial(1, action_probs[0])
except:
chosen_actions = pick_random_action_manually(
action_probs[0])
manual_prob_use += 1
prob_deviation_sum += np.abs(np.sum(action_probs) - 1)
# Saves the selected action for a later use
actions_booleans.append(chosen_actions)
# index of the selected action
action = np.argmax(actions_booleans[-1])
#FOR DEBUGGING
'''
#print("action_probs: " + str(action_probs))
print("observation got: " + str(obsrv))
print("action chosen: " + str(action))
print("manual_prob_use: " + str(manual_prob_use))
print("prob_deviation_sum: " + str(prob_deviation_sum))
print("default_data_counter: " + str(default_data_counter))
print("step_counter: "+str(step_counter))
'''
if (step_counter % WRITE_TO_LOG == 0):
#logger.write_to_log("observation got: " + str(obsrv))
logger.write_to_log("action_probs: " + str(action_probs))
logger.write_to_log("action chosen: " + str(action))
# step the environment and get new measurements
send_action(action, request_id)
# add reward to rewards for a later use in the training step
#rewards.append(reward)
step_counter += 1 #this is for tests
if (step_counter % STEPS_UNTIL_BACKPROP == 0):
update_weights = True
#So the model won't read the same frame many times
time.sleep(0.25)
if (is_dead or update_weights) and len(raw_scores) > 2:
#UPDATE MODEL:
#calculate rewards from raw scores:
#processed_rewards = calc_reward_from_raw(raw_scores,is_dead)
processed_rewards = get_reward(raw_scores, is_dead)
#processed_rewards = raw_score_reward(raw_scores,is_dead)
#FOR DEBUGGING:
if (is_dead):
print('just died!')
# print("processed_rewards: " + str(processed_rewards))
#logger.write_to_log("raw_score: " +str(raw_scores))
logger.write_to_log("processed_rewards: " +
str(processed_rewards))
#'''
# create the rewards sums of the reversed rewards array
rewards_sums = np.cumsum(processed_rewards[::-1])
# normalize prizes and reverse
rewards_sums = decrese_rewards(rewards_sums[::-1])
rewards_sums -= np.mean(rewards_sums)
rewards_sums = np.divide(rewards_sums, np.std(rewards_sums))
#logger.write_to_log("rewards_sums: " + str(rewards_sums))
#'''
modified_rewards_sums = np.reshape(
rewards_sums, [1, len(processed_rewards)])
# modify actions_booleans to be an array of booleans
actions_booleans = np.array(actions_booleans)
actions_booleans = actions_booleans == 1
#FOR DEBUGGING:
'''
fa_res = sess.run(filtered_actions, feed_dict={observations: states, actions_mask: actions_booleans,
rewards_arr: modified_rewards_sums})
pi_res = sess.run(pi, feed_dict={observations: states, actions_mask: actions_booleans,
rewards_arr: modified_rewards_sums})
loss_res = sess.run(loss, feed_dict={observations: states, actions_mask: actions_booleans,
rewards_arr: modified_rewards_sums})
logger.write_to_log("filtered_actions: "+ str(fa_res))
'''
# gradients for current episode
grads = sess.run(Gradients,
feed_dict={
observations: states,
actions_mask: actions_booleans,
rewards_arr: modified_rewards_sums
})
grads_sums += np.array(grads)
episode_number += 1
update_weights = False
#evaluation:
current_average_score = np.average(raw_scores)
average_scores_along_the_game.append(current_average_score)
logger.write_to_log("average score after " +
str(step_counter) + ' steps: ' +
str(current_average_score))
#nullify step_counter:
step_counter = 0
# Do the training step
if (episode_number % BATCH_SIZE == 0):
#if (episode_number % WRITE_TO_LOG == 0):
#logger.write_to_log("learned variables: "+str(vars[0]))
print("taking the update step")
grad_dict = {
Gradients_holder[i]: grads_sums[i]
for i in range(VAR_NO)
}
#TODO choose learning rate?
# take the train step
sess.run(train_step, feed_dict=grad_dict)
#nullify grads_sum
grads_sums = get_empty_grads_sums()
# evaluate and save:
if (best_average_score < current_average_score):
best_average_score = current_average_score
# save with shmickle
with open(BEST_WEIGHTS, 'wb') as f:
pkl.dump(sess.run(tvars), f, protocol=2)
print('Saved best weights successfully!')
# print('Current best result for %d episodes: %f.' % (episode_number, best_average_score))
logger.write_to_log('Saved best weights successfully!')
logger.write_to_log('Current best result for ' +
str(episode_number) + ' episodes: ' +
str(best_average_score))
# manual save
with open(WEIGHTS_FILE, 'wb') as f:
pkl.dump(sess.run(tvars), f, protocol=2)
#print('auto-saved weights successfully.')
# nullify relevant vars and updates episode number.
raw_scores, states, actions_booleans = [BEGINING_SCORE], [], []
manual_prob_use = 0
wait_for_game_to_start()
logger.write_spacer()
plot_graph(average_scores_along_the_game,
"Policy Gradient Average Score During Game",
"PG_avg_score_during_game.png", "Epsisodes No.",
"Average Score")
env.step(action=action, user=user)
# each user changes the inner state of the environment where the environment uses the inner state
# in order to keep track on the channels and the ACK signals for each user
nextStateForEachUser, rewardForEachUser = env.getNextState()
# if a reward is one that means that a packets was successfully delivered over the channel
# the sum has a maximum of the number of channels -> config.K
channelThroughPut = channelThroughPut + np.sum(rewardForEachUser)
# measuring the expected value
channelThroughPut = channelThroughPut / (config.Iterations *
config.TimeSlots)
print("Channel Utilization average {}".format(channelThroughPut))
ToPlotX = range(config.Iterations * config.TimeSlots)
ToPlotY = np.ones_like(ToPlotX) * channelThroughPut
plot_graph(data=[ToPlotX, ToPlotY],
filename="Aloha",
title="Aloha",
xlabel="Time slot",
ylabel="Average channel utilization",
legend="SlottedAloha")
def testEnv():
env = Env()
channelThroughPut = 0 # fraction of time that packets are successfully delivered over the channel
# i.e no collisions or idle time slots
for iteration in range(config.Iterations):
for t in range(config.TimeSlots):
initialState = env.reset()
for user in range(config.N):
action = slottedAlohaProtocol()
env.step(action=action, user=user)
# each user changes the inner state of the environment where the environment uses the inner state
# Loop over k steps
while agent.step_number < MAX_STEPS:
agent.take_one_step()
#write to log and add to plot array:
if (agent.step_number % agent.WRITE_TO_LOG_EVERY == 0):
avg_scores_per_step.append(np.average(agent.last_raw_scores))
logger.write_to_log("avg_scores_per_step" +
str(avg_scores_per_step))
# Save weights and best weights:
#last loaded weights will be override on the first save
if (not agent.TEST_MODE):
agent.save_weights(WEIGHTS_FILE)
if (avg_scores_per_step[-1] > best_avg_per_step):
best_avg_per_step = avg_scores_per_step[-1]
agent.save_weights(BEST_WEIGHTS)
# Evaluation and plotting
plot_graph(avg_scores_per_step, "Average Score Per 100 Steps",
"DQN_avg_score_per_step_by_epoch_" + str(agent.epoch_no),
"Step No.", "Average score")
avg_scores_per_epoch.append(np.average(avg_scores_per_step))
logger.write_to_log("avg_scores_per_epoch" + str(avg_scores_per_epoch))
logger.write_spacer()
agent.epoch_no += 1
plot_graph(avg_scores_per_epoch, "Average Score Per Epoch",
"DQN_avg_score_per_epoch_for_experiment", "Epoch No.",
"Average score")
print("finished experiement")
def main():
logger = get_logger(FLAGS.PoPS_dir + "/PoPS_ITERATIVE")
logger.info(" ------------- START: -------------")
logger.info("Setting initial data structures")
accuracy_vs_size = [[], []]
logger.info("Loading models")
teacher = PongTargetNet(input_size=dense_config.input_size, output_size=dense_config.output_size)
teacher.load_model(path=FLAGS.teacher_path) # load teacher
logger.info("----- evaluating teacher -----")
print("----- evaluating teacher -----")
teacher_score = evaluate(agent=teacher, n_epoch=FLAGS.eval_epochs)
logger.info("----- teacher evaluated with {} ------".format(teacher_score))
print("----- teacher evaluated with {} -----".format(teacher_score))
prune_step_path = FLAGS.PoPS_dir + "/prune_step_"
policy_step_path = FLAGS.PoPS_dir + "/policy_step_"
initial_path = policy_step_path + "0"
copy_weights(output_path=initial_path, teacher_path=FLAGS.teacher_path) # inorder to create the initial model
compressed_agent = StudentPong(input_size=student_config.input_size,
output_size=student_config.output_size,
model_path=initial_path,
tau=student_config.tau, prune_till_death=True,
pruning_freq=prune_config.pruning_freq,
sparsity_end=prune_config.sparsity_end,
target_sparsity=prune_config.target_sparsity)
compressed_agent.load_model()
initial_size = compressed_agent.get_number_of_nnz_params()
accuracy_vs_size[0].append(initial_size)
accuracy_vs_size[1].append(teacher_score)
initial_number_of_params_at_each_layer = compressed_agent.get_number_of_nnz_params_per_layer()
initial_number_of_nnz = sum(initial_number_of_params_at_each_layer)
converge = False
iteration = 0
convergence_information = deque(maxlen=2)
convergence_information.append(100)
precent = 100
arch_type = 0
last_measure = initial_size
while not converge:
iteration += 1
print("----- Pruning Step {} -----".format(iteration))
logger.info(" ----- Pruning Step {} -----".format(iteration))
path_to_save_pruned_model = prune_step_path + str(iteration)
sparsity_vs_accuracy = iterative_pruning_policy_distilliation(logger=logger, agent=compressed_agent,
target_agent=teacher,
iterations=FLAGS.iterations,
config=student_config,
best_path=path_to_save_pruned_model,
arch_type=arch_type)
plot_graph(data=sparsity_vs_accuracy, name=FLAGS.PoPS_dir + "/initial size {}%, Pruning_step number {}"
.format(precent, iteration), figure_num=iteration)
# loading model which has reasonable score with the highest sparsity
compressed_agent.load_model(path_to_save_pruned_model)
# the amount of parameters that are not zero at each layer
nnz_params_at_each_layer = compressed_agent.get_number_of_nnz_params_per_layer()
# the amount of parameters that are not zero
nnz_params = sum(nnz_params_at_each_layer)
# redundancy is the parameters we dont need, nnz_params / initial is the params we need the opposite
redundancy = (1 - nnz_params / initial_number_of_nnz) * 100
print("----- Pruning Step {} finished, got {}% redundancy in net params -----"
.format(iteration, redundancy))
logger.info("----- Pruning Step {} finished , got {}% redundancy in net params -----"
.format(iteration, redundancy))
logger.info("----- Pruning Step {} finished with {} NNZ params at each layer"
.format(iteration, nnz_params_at_each_layer))
print(" ----- Evaluating redundancy at each layer Step {}-----".format(iteration))
logger.info(" ----- Evaluating redundancy at each layer Step {} -----".format(iteration))
redundancy_at_each_layer = calculate_redundancy(initial_nnz_params=initial_number_of_params_at_each_layer,
next_nnz_params=nnz_params_at_each_layer)
logger.info("----- redundancy for each layer at step {} is {} -----".format(iteration, redundancy_at_each_layer))
print(" ----- Creating Model with size according to the redundancy at each layer ----- ")
logger.info("----- Creating Model with size according to the redundancy at each layer -----")
policy_distilled_path = policy_step_path + str(iteration)
# creating the compact model where every layer size is determined by the redundancy measure
compressed_agent = StudentPong(input_size=student_config.input_size,
output_size=student_config.output_size,
model_path=policy_distilled_path,
tau=student_config.tau,
redundancy=redundancy_at_each_layer,
pruning_freq=prune_config.pruning_freq,
sparsity_end=prune_config.sparsity_end,
target_sparsity=prune_config.target_sparsity,
prune_till_death=True,
last_measure=last_measure)
nnz_params_at_each_layer = compressed_agent.get_number_of_nnz_params_per_layer()
logger.info("----- Step {} ,Created Model with {} NNZ params at each layer"
.format(iteration, nnz_params_at_each_layer))
iterative_size = compressed_agent.get_number_of_nnz_params()
precent = (iterative_size / initial_size) * 100
convergence_information.append(precent)
print(" ----- Step {}, Created Model with size {} which is {}% from original size ----- "
.format(iteration, iterative_size, precent))
logger.info("----- Created Model with size {} which is {}% from original size -----"
.format(iterative_size, precent))
if precent > 10:
arch_type = 0
else:
arch_type = 1
print(" ----- policy distilling Step {} ----- ".format(iteration))
logger.info("----- policy distilling Step {} -----".format(iteration))
fit_supervised(logger=logger, arch_type=arch_type, student=compressed_agent, teacher=teacher,
n_epochs=FLAGS.n_epoch)
compressed_agent.load_model(path=policy_distilled_path)
policy_distilled_score = evaluate(agent=compressed_agent, n_epoch=FLAGS.eval_epochs)
compressed_agent.reset_global_step()
print(" ----- policy distilling Step {} finished with score {} ----- "
.format(iteration, policy_distilled_score))
logger.info("----- policy distilling Step {} finished with score {} -----"
.format(iteration, policy_distilled_score))
converge = check_convergence(convergence_information)
accuracy_vs_size[0].append(iterative_size)
accuracy_vs_size[1].append(policy_distilled_score)
plot_graph(data=accuracy_vs_size, name=FLAGS.PoPS_dir + "/accuracy_vs_size", figure_num=iteration + 1, xaxis='NNZ params', yaxis='Accuracy')